Mesoscale Convective Systems in DYAMOND Models: A Feature Tracking Intercomparison.
- 1Pacific Northwest National Laboratory, Richland, USA (zhe.feng@pnnl.gov)
- 2National Center for Atmospheric Research, Boulder, USA (prein@ucar.edu)
- 3The French National Centre for Scientific Research, Paris, France (thomas.fiolleau@cnrs.fr)
- 4University of Oxford, Oxford, UK (william.jones@physics.ox.ac.uk)
- 5NOAA Air Resources Laboratory, Louisville, USA (zachary.moon@noaa.gov)
- 6Ocean University of China, Qingdao, China (songfengfei@ouc.edu.cn)
- 7China Meteorological Administration, Beijing, China (lipx@cma.gov.cn)
- 8University of Leeds, Leeds, UK (B.W.Maybee@leeds.ac.uk)
- 9UK Centre For Ecology & Hydrology, Lancaster, UK (cornkle@ceh.ac.uk)
The DYAMOND project (Stevens et al. 2019) provides an intercomparison framework for state-of-the-art global convection-permitting models with km-scale horizontal grid spacing that can directly simulate convective storms. We recently assessed the fidelity of the convective storms simulated by DYAMOND models using a novel feature tracking technique (Feng et al. 2023) and found a surprisingly large inter-model spread in the simulated frequency of ordinary deep convection and mesoscale convective systems (MCSs), as well as their associated precipitation. Recent works also showed that different feature tracking algorithms have significant impacts on estimating MCS characteristics including frequency, size, lifetime and precipitation (Prein et al. 2023). To further investigate how feature tracking methods affect the evaluation of global MCS simulations and our understanding of convective organization in observations and DYAMOND simulations, we are organizing a new international initiative called MCSMIP (MCS tracking Method Intercomparison Project). Preliminary results from several different feature trackers show that DYAMOND models generally underestimate observed MCS precipitation amount and their contribution to total precipitation in the tropics (Fig. 1), and the simulated MCS precipitation is too intense. However, some models have notable differences in MCS frequency and characteristics among the trackers. Potential paths towards more process-oriented model diagnostics to better understand the differences in simulated MCS and precipitation characteristics will be discussed.
Figure 1. (a) Observed MCS contribution to total precipitation during DYAMOND Phase II, (b) model relative mean difference (%) from observations in the tropics. Each group of bars in (b) is from a feature tracker: PyFLEXTRKR, MOAAP, TOOCAN, tobac, TAMS, and simpleTrack, and each bar denotes a DYAMOND model.
References
Feng, Z. et al. (2023). Mesoscale Convective Systems in DYAMOND Global Convection-Permitting Simulations. Geophys. Res. Lett., doi: 10.1029/2022GL102603.
Prein, A. et al. (2023). Km-Scale Simulations of Mesoscale Convective Systems (MCSs) Over South America – A Feature Tracker Intercomparison. DOI: 10.22541/essoar.169841723.36785590/v1.
How to cite: Feng, Z., Leung, R., Prein, A., Fiolleau, T., Jones, W., Moon, Z., Maybee, B., Song, F., Song, J., Núñez Ocasio, K., Klein, C., Varble, A., Roca, R., and Li, P.: Mesoscale Convective Systems in DYAMOND Models: A Feature Tracking Intercomparison., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1219, https://doi.org/10.5194/egusphere-egu24-1219, 2024.
